Laser composite surfacing of AISI 304 stainless steel with titanium boride for improved wear resistance

The present study concerns development of a hard in situ boride-dispersed composite layer on the surface of AISI 304 stainless steel substrate to improve the wear resistance property. Laser processing was carried out by melting the surface of sand-blasted AISI 304 stainless steel substrate using a continuous wave CO₂ laser and simultaneous deposition of a mixture of K₂TiF₆ (potassium titanium hexafluoride) and KBF₆ (potassium hexafloroborate) (in the weight ratio of 2:1) using Ar as shrouding environment. Powder feed rate was maintained constant at 4 g/min. Irradiation results in dissociation of a pre-deposited mixture along with a part of the stainless steel substrate, intermixing and rapid solidification to form the composite layer on the surface. The micro-structure of composite layer consists of dispersion of titanium boride particles in AISI 304 stainless steel matrix. Volume fraction of particles is found to be uniform throughout the composite layer, though varied with laser parameters. The micro-hardness of the surface was improved 250–350 VHN as compared to 220 VHN of the AISI 304 stainless steel substrate with a significant improvement in wear resistance property. The mechanism of wear was found to be a combination of adhesive and abrasive in as-received stainless steel. However, it was predominantly abrasive for laser composite surfaced stainless steel.     

Gender bias in journals of gender studies

The causes of gender bias favoring men in scientific and scholarly systems are complex and related to overall gender relationships in most of the countries of the world. An as yet unanswered question is whether in research publication gender bias is equally distributed over scientific disciplines and fields or if that bias reflects a closer relation to the subject matter. We expected less gender bias with respect to subject matter, and so analysed 14 journals of gender studies using several methods and indicators. The results confirm our expectation: the very high position of women in co-operation is striking; female scientists are relatively overrepresented as first authors in articles. Collaboration behaviour in gender studies differs from that of authors in PNAS. The pattern of gender studies reflects associations between authors of different productivity, or ??masters?? and ??apprentices?? but the PNAS pattern reflects associations between authors of roughly the same productivity, or ??peers??. It would be interesting to extend the analysis of these three-dimensional collaboration patterns further, to see whether a similar characterization holds, what it might imply about the patterns of authorship in different areas, what those patterns might imply about the role of collaboration, and whether there are differences between females and males in collaboration patterns.     

Expression of Aspergillus nidulans phy Gene in Nicotiana benthamiana Produces Active Phytase with Broad Specificities

A full-length phytase gene (phy) of Aspergillus nidulans was amplified from the cDNA library by polymerase chain reaction (PCR), and it was introduced into a bacterial expression vector, pET-28a. The recombinant protein (rPhy-E, 56 kDa) was overexpressed in the insoluble fraction of Escherichia coli culture, purified by Ni-NTA resin under denaturing conditions and injected into rats as an immunogen. To express A. nidulans phytase in a plant, the full-length of phy was cloned into a plant expression binary vector, pPZP212. The resultant construct was tested for its transient expression by Agrobacterium-infiltration into Nicotiana benthamiana leaves. Compared with a control, the agro-infiltrated leaf tissues showed the presence of phy mRNA and its high expression level in N. benthamiana. The recombinant phytase (rPhy-P, 62 kDa) was strongly reacted with the polyclonal antibody against the nonglycosylated rPhy-E. The rPhy-P showed glycosylation, two pH optima (pH 4.5 and pH 5.5), an optimum temperature at 45~55 °C, thermostability and broad substrate specificities. After deglycosylation by peptide-N-glycosidase F (PNGase-F), the rPhy-P significantly lost the phytase activity and retained 1/9 of the original activity after 10 min of incubation at 45 °C. Therefore, the deglycosylation caused a significant reduction in enzyme thermostability. In animal experiments, oral administration of the rPhy-P at 1500 U/kg body weight/day for seven days caused a significant reduction of phosphorus excretion by 16% in rat feces. Besides, the rPhy-P did not result in any toxicological changes and clinical signs.     

Designing polyethylene terephthalate‐based barrier resins

We have studied the efficiencies of commercially available barrier resins used for food packaging and beverage bottle applications and compared them with resins made using copolymer and blending approaches. The effects of various copolymer related parameters that influence oxygen barrier performance and the underlying mechanisms for these effects in PET‐based copolymers and blends were investigated. Using these approaches, it is possible to make highly effective PET‐based copolymers for gas barrier applications. Blending of PET with phenolic and other organic materials shows equivalent barrier performance in PET copolymers having comonomer levels of <8‐mole %. Reduction in oxygen permeation rates results from the reduction or elimination of short range molecular motion of polyethylene terephthalate molecules. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Depth Sensing Using Geometrically Constrained Polarization Normals

Analyzing the polarimetric properties of reflected light is a potential source of shape information. However, it is well-known that polarimetric information contains fundamental shape ambiguities, leading to an underconstrained problem of recovering 3D geometry. To address this problem, we use additional geometric information, from coarse depth maps, to constrain the shape information from polarization cues. Our main contribution is a framework that combines surface normals from polarization (hereafter polarization normals) with an aligned depth map. The additional geometric constraints are used to mitigate physics-based artifacts, such as azimuthal ambiguity, refractive distortion and fronto-parallel signal degradation. We believe our work may have practical implications for optical engineering, demonstrating a new option for state-of-the-art 3D reconstruction.     

Fluorescent Probe DCVJ Shows High Sensitivity for Characterization of Amyloid β-Peptide Early in the Lag Phase

The aggregation of intrinsically disordered and misfolded proteins in the form of oligomers and fibrils plays a crucial role in a number of neurological and neurodegenerative diseases. Currently, most probes and biophysical techniques that detect and characterize fibrils at high resolution fail to show sensitivity and binding for oligomers. Here, we show that 9-(dicyano-vinyl)julolidine (DCVJ), a class of molecular rotor, binds amyloid beta (Aβ) early aggregates, and we report the kinetics as well as packing of the oligomer formation. The binding of DCVJ to Aβ40 increased its emission intensity with time at 510 nm and produced a second excimer peak at 575 nm. However, DCVJ did not bind to the prefibrillar aggregates of Aβ42, which indicated that the oligomers formed by Aβ40 and Aβ42 were not the same. The F4C F19W mutant of Aβ40, which did not form fibrils, also bound DCVJ, but the emission spectral profile varied from that of the wild-type (WT). Atomic force microscopy images of WT Aβ40, the F4C F19W mutant, and Aβ42 oligomers displayed differences in size and shape, confirming the difference in their DCVJ spectra. The effect of epigallocatechin-3-gallate (EGCG) on the reduction of Aβ42 fibrils was also observed with finer detail than with other techniques. The results of this study show that DCVJ detects early aggregates and provides valuable information regarding the oligomer kinetics, packing, and mechanism of formation.     

Every team deserves a second chance: an extended study on predicting team performance

Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance in finding the correct answer to complex problems. We present a novel benefit of voting, that has not been observed before: we can use the voting patterns to assess the performance of a team and predict their final outcome. This prediction can be executed at any moment during problem-solving and it is completely domain independent. Hence, it can be used to identify when a team is failing, allowing an operator to take remedial procedures (such as changing team members, the voting rule, or increasing the allocation of resources). We present three main theoretical results: (1) we show a theoretical explanation of why our prediction method works; (2) contrary to what would be expected based on a simpler explanation using classical voting models, we show that we can make accurate predictions irrespective of the strength (i.e., performance) of the teams, and that in fact, the prediction can work better for diverse teams composed of different agents than uniform teams made of copies of the best agent; (3) we show that the quality of our prediction increases with the size of the action space. We perform extensive experimentation in two different domains: Computer Go and Ensemble Learning. In Computer Go, we obtain high quality predictions about the final outcome of games. We analyze the prediction accuracy for three different teams with different levels of diversity and strength, and show that the prediction works significantly better for a diverse team. Additionally, we show that our method still works well when trained with games against one adversary, but tested with games against another, showing the generality of the learned functions. Moreover, we evaluate four different board sizes, and experimentally confirm better predictions in larger board sizes. We analyze in detail the learned prediction functions, and how they change according to each team and action space size. In order to show that our method is domain independent, we also present results in Ensemble Learning, where we make online predictions about the performance of a team of classifiers, while they are voting to classify sets of items. We study a set of classical classification algorithms from machine learning, in a data-set of hand-written digits, and we are able to make high-quality predictions about the final performance of two different teams. Since our approach is domain independent, it can be easily applied to a variety of other domains.